| import torch |
| import torch.nn as nn |
| from .. import SparseTensor |
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|
| class SparseConv3d(nn.Module): |
| def __init__(self, in_channels, out_channels, kernel_size, stride=1, dilation=1, bias=True, indice_key=None): |
| super(SparseConv3d, self).__init__() |
| if 'torchsparse' not in globals(): |
| import torchsparse |
| self.conv = torchsparse.nn.Conv3d(in_channels, out_channels, kernel_size, stride, 0, dilation, bias) |
|
|
| def forward(self, x: SparseTensor) -> SparseTensor: |
| out = self.conv(x.data) |
| new_shape = [x.shape[0], self.conv.out_channels] |
| out = SparseTensor(out, shape=torch.Size(new_shape), layout=x.layout if all(s == 1 for s in self.conv.stride) else None) |
| out._spatial_cache = x._spatial_cache |
| out._scale = tuple([s * stride for s, stride in zip(x._scale, self.conv.stride)]) |
| return out |
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|
| class SparseInverseConv3d(nn.Module): |
| def __init__(self, in_channels, out_channels, kernel_size, stride=1, dilation=1, bias=True, indice_key=None): |
| super(SparseInverseConv3d, self).__init__() |
| if 'torchsparse' not in globals(): |
| import torchsparse |
| self.conv = torchsparse.nn.Conv3d(in_channels, out_channels, kernel_size, stride, 0, dilation, bias, transposed=True) |
|
|
| def forward(self, x: SparseTensor) -> SparseTensor: |
| out = self.conv(x.data) |
| new_shape = [x.shape[0], self.conv.out_channels] |
| out = SparseTensor(out, shape=torch.Size(new_shape), layout=x.layout if all(s == 1 for s in self.conv.stride) else None) |
| out._spatial_cache = x._spatial_cache |
| out._scale = tuple([s // stride for s, stride in zip(x._scale, self.conv.stride)]) |
| return out |
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